PROJECT SUMMARY Understanding the genetic factors and pathways that underlie variation in the aging and cognitive function of the brain is crucial for diagnosis, prevention, and treatment of age-related deficits in cognitive function and Alzheimer?s disease (AD). However, discovery of these genetic factors and pathways in humans has been impeded by the scope and cost of obtaining a sufficient amount of longitudinal data to analyze the wide- ranging heterogeneity of cognitive and neurophysiological changes, including molecular data at the early asymptomatic stages of disease. Analyses of genetically diverse mice across the lifespan can directly circumvent these limitations as such mouse populations are a more practical model for the genetic and etiological complexity of the aging human population. The original proposal was to leverage existing RNAseq data generated from the hippocampus of aging recombinant inbred BXD strains to identify gene transcriptional networks associated with working memory and contextual fear deficits. However, data analyzed as part of the parent grant showed that the enhanced genetic diversity in the Diversity Outbred (DO) mouse population is likely to identify additional gene variants involved in mediating susceptibility to age-related cognitive decline and dementia that aren?t segregating in the BXD mice. The specific aim of this supplement is to identify genes and molecular networks associated with working memory decline in a mouse population of normal aging, and cross-check these with relevant human data, in order to identify translationally relevant targets to promote cognitive longevity. In this supplement, additional funding is sought for new RNAseq analysis of biobanked hippocampi from a large cohort of 487 DO mice at 6, 12 and 18 months of age. Expression QTLs (eQTLs) and regulatory networks that determine resilience to cognitive aging will be defined using the genetic variability in the DO mice (Aim S1a) and susceptibility and resilience factors and networks will be predicted using causal inference analyses of multidimensional data (DNA, RNA and behavior) (Aim S1b). These contributions are significant because a mechanistic understanding of the role of resilience genes could offer a route for therapeutic intervention for AD possibly by slowing or preventing cognitive impairment. Linking gene expression signals with the range of low-to-high risk variants in mouse models that recapitulate a spectrum of cognitive aging is highly attainable and will advance the identification of early stage molecular changes that determine variability in cognitive endpoints in normal aging as well as AD.